GO Term Enrichment for Top 5% MCC-Ranked Breast Cancer Genes

Takanori Sasaki published this dataset on 2026-05-06. It contains gene-ontology (GO) term enrichment results for the top 5% of genes ranked by maximum clique centrality (MCC) within breast cancer protein-protein interaction networks. The analysis identified 11 novel candidate biomarkers and found significant enrichment for terms related to the cell cycle and fibroblast growth factor receptor signaling.

Use Cases

• Prioritizing novel breast cancer biomarker candidates based on MCC ranking patterns described in the study
• Investigating the association between gene-ontology terms related to the cell cycle and network centrality
• Validating topological features of known clinical biomarkers within protein-protein interaction networks
• Comparing biomarker identification performance of MCC against other centrality measures mentioned in the description

Strengths

• Dataset is derived from a structured analysis using 12 distinct centrality measures on protein-protein interaction networks
• Results are specific, identifying 11 novel candidate biomarkers and significant GO term enrichments
• Published under a permissive CC-BY-4.0 license for reuse

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset is very small at 9.5 KB, indicating limited scope

Provenance

Source

figshare, author Takanori Sasaki

Collection Method

Derived from centrality analysis of breast cancer-associated protein-protein interaction networks constructed from the STRING and DISEASES databases.

Time Range

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Freshness

Last updated 2026-05 06 17:28:27; freshness should be verified

Geography

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